US2014107977A1PendingUtilityA1
Condition diagnosing method and condition diagnosing device
Est. expiryOct 16, 2032(~6.2 yrs left)· nominal 20-yr term from priority
G06N 20/10G06F 11/30G06N 20/00G05B 23/024
36
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Claims
Abstract
A condition diagnosing method capable of executing condition diagnosis considering a secular change is provided. A condition diagnosing method includes a first diagnosing step of determining presence or absence of abnormality in diagnosis data by a latest one class support vector machine, and diagnosing the diagnosis data determined as abnormal as relating to a failure, and a second diagnosing step of determining presence or absence of abnormality in the diagnosis data determined as abnormal in the first diagnosing step by an initial one class support vector machine, diagnosing the diagnosis data determined as abnormal as relating to secular deterioration, and diagnosing the diagnosis data determined as not abnormal as normal.
Claims
exact text as granted — not AI-modifiedWhat is claimed is:
1 . A condition diagnosing method comprising:
a first diagnosing step of determining presence or absence of abnormality in diagnosis data by a latest one class support vector machine, and diagnosing the diagnosis data determined as abnormal as relating to a failure; and a second diagnosing step of determining presence or absence of abnormality in the diagnosis data determined as abnormal in the first diagnosing step by an initial one class support vector machine, diagnosing the diagnosis data determined as abnormal as relating to secular deterioration, and diagnosing the diagnosis data determined as not abnormal as normal, wherein the latest one class support vector machine is constructed by performing additional learning with the diagnosis data obtained from a diagnosis target at the time of the diagnosis, and the initial one class support vector machine is constructed by training with the data obtained when the diagnosis target was initially manufactured.
2 . A condition diagnosing method comprising:
a third diagnosing step of determining presence or absence of abnormality in diagnosis data by an initial one class support vector machine, and diagnosing the diagnosis data determined as not abnormal as normal; and a fourth diagnosing step of determining presence or absence of abnormality in the diagnosis data determined as abnormal in the third diagnosing step by a latest one class support vector machine, diagnosing the diagnosis data determined as abnormal as relating to a failure, and diagnosing the diagnosis data determined as not abnormal as relating to secular deterioration, wherein the latest one class support vector machine is constructed by performing additional learning with the diagnosis data obtained from a diagnosis target at the time of the diagnosis, and the initial one class support vector machine is constructed by training with the data obtained when the diagnosis target was initially manufactured.
3 . The condition diagnosing method according to claim 1 , wherein
in the additional learning, a distance between the added diagnosis data and a previous normal region is handled as an evaluation function, and a kernel parameter σ of the latest one class support vector machine is updated.
4 . The condition diagnosing method according to claim 2 , wherein
in the additional learning, a distance between the added diagnosis data and a previous normal region is handled as an evaluation function, and a kernel parameter σ of the latest one class support vector machine is updated.
5 . The condition diagnosing method according to claim 3 , wherein
the kernel parameter σ is not updated when a maximum value of a result of arithmetic of the evaluation function with the added diagnosis data is equal to or lower than a predetermined threshold.
6 . The condition diagnosing method according to claim 4 , wherein
the kernel parameter σ is not updated when a maximum value of a result of arithmetic of the evaluation function with the added diagnosis data is equal to or lower than a predetermined threshold.
7 . The condition diagnosing method according to claim 3 , wherein
the additional learning handles the diagnosis data not included in the previous normal region as targets of the additional learning, and excludes the diagnosis data included in the previous normal region from the targets of the additional learning.
8 . The condition diagnosing method according to claim 4 , wherein
the additional learning handles the diagnosis data not included in the previous normal region as targets of the additional learning, and excludes the diagnosis data included in the previous normal region from the targets of the additional learning.
9 . The condition diagnosing method according to claim 1 , wherein
one or both of the latest one class support vector machine and the initial one class support vector machine is constructed by applying the kernel specified in the following formula (8), provided that m is 1, 2, 3, . . . M.
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10 . The condition diagnosing method according to claim 2 , wherein
one or both of the latest one class support vector machine and the initial one class support vector machine is constructed by applying the kernel specified in the following formula (8), provided that m is 1, 2, 3, . . . M.
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11 . A condition diagnosing device comprising:
a first diagnosing unit determining presence or absence of abnormality in diagnosis data by a latest one class support vector machine, and diagnosing the diagnosis data determined as abnormal as relating to a failure; and a second diagnosing unit of determining presence or absence of abnormality in the diagnosis data determined as abnormal by the first diagnosing unit by an initial one class support vector machine, diagnosing the diagnosis data determined as abnormal as relating to secular deterioration, and diagnosing the diagnosis data determined as not abnormal as normal, wherein the latest one class support vector machine is constructed by performing additional learning with the diagnosis data obtained from a diagnosis target at the time of the diagnosis, and the initial one class support vector machine is constructed by training with the data obtained when the diagnosis target was initially manufactured.
12 . A condition diagnosing method comprising:
a third diagnosing unit of determining presence or absence of abnormality in diagnosis data by an initial one class support vector machine, and diagnosing the diagnosis data determined as not abnormal as normal; and a fourth diagnosing unit of determining presence or absence of abnormality in the diagnosis data determined as abnormal by the third diagnosing unit by a latest one class support vector machine, diagnosing the diagnosis data determined as abnormal as relating to a failure, and diagnosing the diagnosis data determined as not abnormal as relating to secular deterioration, wherein the latest one class support vector machine is constructed by performing additional learning with the diagnosis data obtained from a diagnosis target at the time of the diagnosis, and the initial one class support vector machine is constructed by training with the data obtained when the diagnosis target was initially manufactured.Join the waitlist — get patent alerts
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